Enterprises continue to amass large amounts of data related to their business, their employees, their partners, and their customers. The data may reside in similar or disparate databases or similar or disparate storage locations. Furthermore, the data may be accessed for a variety of reasons, in a variety of permutations, and by a variety of resources within the enterprises.
Often the data is acquired via database search queries. Each time the same data is accessed for some desired purpose, an existing query or an entirely new query is reconstructed for purposes of acquiring that data. This does not facilitate efficient reuse within the enterprise and does not facilitate ease of use, since accessing the data still requires knowledge about the structure of the data source that contains the desired data.
That is, business analysts cannot reference desired data as a logical piece of information; rather, the business analyst must have some knowledge about the data source and the structure of that data source if the analyst expects to construct a query to acquire the desired data from the data source. Consequently, business analysts must also be somewhat skilled in database languages and interfaces and must have some training in the structure of data, if they expect to independently acquire their desired data without technical assistance.
This is not efficient and is not necessary, since business analysts should be skilled and trained in the business operations and not necessarily in database management, which should be reserved for highly trained technical staff.
Thus, it can be seen that improved techniques for data acquisition from data sources are desirable.